Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/15413#discussion_r95064061
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/clustering/GaussianMixtureSuite.scala
---
@@ -126,9 +143,93 @@ class GaussianMixtureSuite extends SparkFunSuite with
MLlibTestSparkContext
testEstimatorAndModelReadWrite(gm, dataset,
GaussianMixtureSuite.allParamSettings, checkModelData)
}
+
+ test("univariate dense/sparse data with two clusters") {
+ val weights = Array(2.0 / 3.0, 1.0 / 3.0)
+ val means = Array(Vectors.dense(5.1604), Vectors.dense(-4.3673))
+ val covs = Array(Matrices.dense(1, 1, Array(0.86644)),
Matrices.dense(1, 1, Array(1.1098)))
+ val gaussians = means.zip(covs).map { case (mean, cov) =>
+ new MultivariateGaussian(mean, cov)
+ }
+ val expected = new GaussianMixtureModel("dummy", weights, gaussians)
+
+ Seq(denseDataset, sparseDataset).foreach { dataset =>
+ val actual = new GaussianMixture().setK(2).setSeed(seed).fit(dataset)
+ modelEquals(expected, actual)
+ }
+ }
+
+ test("check distributed decomposition") {
--- End diff --
This test only checks that when we distribute the computation that it
produces a model, i.e. that it doesn't fail. So, AFAICT we don't have any test
right now that checks that when we distribute the computation it produces a
_correct_ model. I think it's a good idea to have that here.
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